BİLGİ PAKETİ / DERS KATALOĞU
1410002009 Natural Language Processing
Degree Programs
COMPUTER ENGINEERING
Diploma SupplementNational Qualifications
COMPUTER ENGINEERING
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Ders Genel Tanıtım Bilgileri

Course Code: 1410002009
Ders İsmi: Natural Language Processing
Ders Yarıyılı: Spring
Ders Kredileri:
Theoretical Practical Credit ECTS
3 0 3 5
Language of instruction: TR
Ders Koşulu:
Ders İş Deneyimini Gerektiriyor mu?: No
Type of course: Bölüm Seçmeli
Course Level:
Bachelor TR-NQF-HE:6. Master`s Degree QF-EHEA:First Cycle EQF-LLL:6. Master`s Degree
Mode of Delivery: Face to face
Course Coordinator : Dr.Öğr.Üyesi Recep DURANAY
Course Lecturer(s):
Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives: To meet with natural language and its application areas; Implementing possible applications
Course Content: Morphological analysis of language; Different grammatical structures; Clustering and Classification Algorithms; Information Extraction; Question Answering; Natural Language Processing Applications

Learning Outcomes

The students who have succeeded in this course;
Learning Outcomes
1 - Knowledge
Theoretical - Conceptual
1) The student will know the conveniences of using natural language in computer environment to our daily life.
2 - Skills
Cognitive - Practical
3 - Competences
Communication and Social Competence
Learning Competence
1) The student will learn algorithms and methods used in natural language processing and develop applications.
Field Specific Competence
1) The student will be able to recognize and use the tools developed abroad and in the country.
2) The student will develop a project using what they learned in the lesson.
Competence to Work Independently and Take Responsibility
1) The student will learn all the concepts in natural language processing.

Ders Akış Planı

Week Subject Related Preparation
1) Introduction to Natural Language Processing
2) Fundamentals of Linguistics and Language Models
3) Syntactic Analysis and Morphological Analysis
4) Introduction to Machine Learning
5) Regular Expressions
6) Finding an Entity Name
7) Text Classification
8) Midterm
9) String Algorithms
10) Hidden Markov Models and Applications
11) Information Extraction
12) Text Indexing and Access
13) Question Answering
14) Collocation
15) Final

Sources

Course Notes / Textbooks: Natural Language Understanding, J.Allen, Benjamin-Cummings
Speech and Language Processing, Jurafsky and Martin, Prentice Hall
Foundations of Statistical Natural Language Processing, C. D. Manning, H. Schütze, MIT
Handbook of Natural Language Processing, R. Dale, H. Moisl, H.Somers,Marcel Dekker
References: Natural Language Understanding, J.Allen, Benjamin-Cummings
Speech and Language Processing, Jurafsky and Martin, Prentice Hall
Foundations of Statistical Natural Language Processing, C. D. Manning, H. Schütze, MIT
Handbook of Natural Language Processing, R. Dale, H. Moisl, H.Somers,Marcel Dekker

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları

1

2

3

5

4
Program Outcomes
1) PO 1.1) Sufficient knowledge in mathematics, science and computer engineering
2) PO 1.2) Ability to apply theoretical and applied knowledge in mathematics, science and computer engineering for modeling and solving engineering problems.
3) PO 2.1) Identifying complex engineering problems
4) PO 2.2) Defining complex engineering problems
5) PO 2.3) Formulating complex engineering problems
6) PO 2.4) Ability to solve complex engineering problems
7) PO 2.5) Ability to choose and apply appropriate analysis and modeling methods
8) PO 3.1) Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions.
9) PO 3.2) Ability to apply modern design methods under realistic constraints and conditions for a complex system, process, device or product
10) PO 4.1) Developing modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications
11) PO 4.2) Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications
12) PO 4.3) Ability to use information technologies effectively.
13) PO 5.1) Examination of complex engineering problems or discipline-specific research topics, designing experiments
14) PO 5.2) Examination of complex engineering problems or discipline-specific research topics, experimentation
15) PO 5.3 ) Analysis of complex engineering problems or discipline-specific research topics, data collection
16) PO 5.4) Analyzing the results of complex engineering problems or discipline-specific research topics
17) PO 5.5) Examining and interpreting complex engineering problems or discipline-specific research topics

Ders - Öğrenme Kazanımı İlişkisi

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) PO 1.1) Sufficient knowledge in mathematics, science and computer engineering
2) PO 1.2) Ability to apply theoretical and applied knowledge in mathematics, science and computer engineering for modeling and solving engineering problems.
3) PO 2.1) Identifying complex engineering problems
4) PO 2.2) Defining complex engineering problems
5) PO 2.3) Formulating complex engineering problems 5
6) PO 2.4) Ability to solve complex engineering problems
7) PO 2.5) Ability to choose and apply appropriate analysis and modeling methods
8) PO 3.1) Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions.
9) PO 3.2) Ability to apply modern design methods under realistic constraints and conditions for a complex system, process, device or product
10) PO 4.1) Developing modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications
11) PO 4.2) Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications
12) PO 4.3) Ability to use information technologies effectively.
13) PO 5.1) Examination of complex engineering problems or discipline-specific research topics, designing experiments
14) PO 5.2) Examination of complex engineering problems or discipline-specific research topics, experimentation
15) PO 5.3 ) Analysis of complex engineering problems or discipline-specific research topics, data collection
16) PO 5.4) Analyzing the results of complex engineering problems or discipline-specific research topics
17) PO 5.5) Examining and interpreting complex engineering problems or discipline-specific research topics

Öğrenme Etkinliği ve Öğretme Yöntemleri

Ölçme ve Değerlendirme Yöntemleri ve Kriterleri

Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama)
Homework
Bireysel Proje
Sunum

Assessment & Grading

Semester Requirements Number of Activities Level of Contribution
total %
PERCENTAGE OF SEMESTER WORK % 0
PERCENTAGE OF FINAL WORK %
total %

İş Yükü ve AKTS Kredisi Hesaplaması

Activities Number of Activities Duration (Hours) Workload
Course Hours 40 3 120
Presentations / Seminar 1 10 10
Project 1 80 80
Midterms 1 5 5
Final 1 7 7
Total Workload 222